How to Learn AI From Scratch in 2023: A Complete Guide From the Experts
Find out everything you need to know about learning AI in 2023, from tips to get you started, helpful resources, and insights from industry experts.
We’re living through what is quite possibly a pivotal point in human history, where the importance of Artificial Intelligence (AI) is becoming increasingly undeniable. Just consider this statistic: 97% of business owners expect that ChatGPT will bring about positive changes in at least one area of their business, according to a survey by Forbes Advisor. Moreover, tools like ChatGPT, Midjourney, and Bard are ushering AI into the mainstream. This makes the art and science of AI more relevant than ever before.
If you’re an aspiring data scientist, machine learning engineer, AI researcher, or simply an AI enthusiast, this guide is for you. Throughout this article, we’ll detail how to learn AI from scratch and offer insights from industry experts to help steer your journey. As well as covering the skills and tools you’ll need to master, we also explore how businesses can leverage AI in today’s landscape.
What is Artificial Intelligence (AI)?
AI, or Artificial Intelligence, is a branch of computer science focused on creating systems that can perform tasks that would normally require human intelligence. These tasks range from understanding natural language, recognizing patterns, making decisions, and learning from experience. AI is a broad field with numerous subfields, each with its unique objectives and specializations. Check out our full guide, What is AI? to find out more. You can also explore how AI is different from machine learning in a separate article.
What are the different types of artificial intelligence?
As AI grows in popularity, the technology is discussed in various ways. To simplify the remainder of the article, it’s important to look at the different types of AI. AI can be categorized into three levels based on its capabilities:
Artificial Narrow Intelligence (ANI): This is the most common form of AI we interact with today. ANI is designed to perform a single task, like voice recognition or recommendations on streaming services.
Artificial General Intelligence (AGI): An AI with AGI possesses the ability to understand, learn, adapt, and implement knowledge across a wide range of tasks at a human level. While large language models and tools such as ChatGPT have shown the ability to generalize across many tasks—as of 2023, this is still a theoretical concept.
Artificial Super Intelligence (ASI): The final level of AI, ASI, refers to a future scenario where AI surpasses human intelligence in nearly all economically valuable work. This concept, while intriguing, remains largely speculative.
The difference between data science, artificial intelligence, machine learning & deep learning
If you are new to this topic, you may also see the terms “machine learning,” “deep learning,” “data science,” and others creep into AI discourse. AI is a broad field with several subsets, including Machine Learning (ML) and Deep Learning (DL).
While there isn’t an official definition for any of these terms, and while experts argue over the exact boundaries, there is a growing consensus on the broad scope of each term. Here’s a breakdown of how these terms can be defined:
Artificial intelligence refers to computer systems that can behave intelligently, reason, and learn like humans.
Machine learning is a subset of artificial intelligence focused on developing algorithms with the ability to learn without explicitly being programmed.
Deep learning is a subset of machine learning. It is responsible for many of the awe-inspiring news stories about AI in the news (e.g., self-driving cars, ChatGPT). Deep learning algorithms are inspired by the brain’s structure and work exceptionally well with unstructured data such as images, videos, or text.
Data science is a cross-disciplinary field that uses all of the above, amongst other skills like data analysis, statistics, data visualization, and more, to get insight from data.